On the convergence of quasi-random sampling/importance resampling

Authors:
Bart Vandewoestyne;Ronald Cools
Affiliations:
Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, B-3001 Heverlee, Belgium;Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, B-3001 Heverlee, Belgium
Venue:
Mathematics and Computers in Simulation
Year:
2010

Citing 4
Cited 1

Random number generation and quasi-Monte Carlo methods

Random number generation and quasi-Monte Carlo methods
Quasi-Monte Carlo algorithms for unbounded, weighted integration problems

Journal of Complexity
Error reduction techniques in quasi-monte carlo integration

Mathematical and Computer Modelling: An International Journal
Importance resampling for global illumination

EGSR'05 Proceedings of the Sixteenth Eurographics conference on Rendering Techniques

An empirical fur shader

ACM SIGGRAPH ASIA 2010 Sketches

Quantified Score

Hi-index	0.00

Visualization

Abstract

This article discusses the general problem of generating representative point sets from a distribution known up to a multiplicative constant. The sampling/importance resampling (SIR) algorithm is known to be useful in this context. Moreover, the quasi-random sampling/importance resampling (QSIR) scheme, based on quasi-Monte Carlo methods, is a more recent modification of the SIR algorithm and was empirically shown to have better convergence. By making use of quasi-Monte Carlo theory, we derive upper bounds for the error of the QSIR scheme.